Over 90% of missense variants across ~4,000 disease-associated genes are variants of uncertain significance (VUS). Experimental variant effect measurements provide critical evidence about pathogenicity and inform disease biology, but most variants lack data and clinical translation has been limited. The Impact of Genomic Variation on Function Consortium generated experimental data for 62,215 variants across ten genes using multiplexed assays and 1,407 variants across 163 genes using arrayed assays, curated 193,139 additional community-generated variant effect measurements across 30 additional genes, and developed automated calibration methods for translating experimental data and variant effect predictions into clinical evidence. To reduce current VUS, we developed a scalable workflow using only experimental and predictive evidence, enabling reclassification of 75% of the 16,115 VUS in these genes as pathogenic or benign with <1% error. To minimize future VUS, we analyzed >90,000 unobserved variants; 62% had enough evidence to be "preclassified" as pathogenic or benign. We validated our data, evidence and classifications using All of Us and created interactive resources to enable clinical use of the calibrated data. Thus, for 40 genes, representing 1% of the clinical genome, we resolve most existing VUS and future variants, illustrating how systematic use of scalable evidence can empower genomic medicine.

Genomic variation between individuals is essential for understanding how differences in the genome sequence affect molecular and cellular processes. The Impact of Genomic Variation on Function (IGVF) Consortium aims to uncover the relationships among genomic variation, genome function, and phenotypes by combining experimental techniques, such as single-cell mapping and genomic perturbation assays, with computational approaches such as machine learning-based predictive modeling. The IGVF Data and Administrative Coordinating Centers collect, analyze, and disseminate data and results from across the consortium through an open-source platform called the IGVF Catalog. This resource includes, but is not limited to, data on the effects of coding variants on protein abundance and function, noncoding variants on enhancer activity (measured by MPRA or predicted computationally), and associations between variants and quantitative traits. All data are organized within a graph database comprising over 50 types of data collections with nearly 3 billion nodes and over 7.5 billion edges. The Catalog offers public API endpoints (https://api.catalogkg.igvf.org/) and a user-friendly interface for exploring, querying, and visualizing the data at https://catalog.igvf.org. We expect that this open-access platform will support the broader scientific community to advance our understanding of how genomic variation influences biology and disease.

In 2024, individuals funded by NHGRI to support genomic community resources completed a Self-Assessment Tool (SAT) to evaluate their application of the FAIR (Findable, Accessible, Interoperable, and Reusable) principles and assess their sustainability. By collecting insights from the self-administered questionnaires and conducting personal interviews, a valuable perspective was gained on the FAIRness and sustainability of the NHGRI resources. The results highlighted several challenges and key areas the NHGRI resource community could improve by working together to form recommendations to address these challenges. The next step was the formation of an Organizing Committee to identify which challenges could lead to best practices or guidelines for the community. The workshop's Organizing Committee comprised four members from the NHGRI resource community: Carol Bult, PhD, Chris Mungall, PhD, Heidi Rehm, PhD, and Michael Schatz, PhD. In December 2024, the Organizing Committee engaged with the NHGRI resource community to refine these challenges further, inviting feedback on potential focus areas for a future workshop. This collaborative approach led to two informative webinars in December 2024, highlighting specific challenges in data curation, data processing, metadata tools, and variant identifiers within the NHGRI resources. Throughout the workshop planning process, the four Organizing Committee members worked together to create and develop themes, design breakout sessions, and create a detailed agenda. The workshop's agenda was intentionally structured to ensure participants could generate implementable recommendations for the NHGRI resource community. The two-day workshop was held in Bethesda, MD, on March 3-4, 2025. The challenges received from NHGRI resources were classified into four key categories, forming the basis of the workshop. The four key categories are variant identifiers, data processing, data curation, and metadata tools. They are briefly described below, with greater details on their challenges and recommendations in subsequent sections. Metadata Tools:While metadata is vital for capturing context in genomic datasets, its usage and relevance can vary by domain, making it difficult to standardize usage. While various methods exist for annotating and extracting metadata, incomplete or inconsistent annotations often result in ineffective data sharing and interoperability, further reducing data usability and reproducibility.Data Curation:Curation of annotations for genomics data is critical for FAIR-ness. Scalable curation solutions are challenging because of the multiple components for curation, including harmonizing data sets, data cleaning, and annotation. The workshop focused on identifying which aspects of data curation could be streamlined using computational methods while considering the barriers to increased automation.Variant Identifiers:Variant identifiers are standardized representations of genetic variants, crucial for sharing and interpreting genomic data in research and clinical work. They ensure consistent referencing and enable data aggregation. Standardizing variant identifiers is difficult due to varied formats, complex data, and distinct environments for generating and disseminating data.Data Processing:Data processing is a necessary first step in a FAIR environment. As there are many variant workflows, streamlining this process will ensure greater accuracy, reproducibility, interoperability, and FAIRness, driving advancements in clinical research. The workshop focused on addressing these aspects with a key focus on improvements and best practices around data processing for an NHGRI resource. Several recommendations were made throughout the workshop's interactive sessions with the resources' participants. While many recommendations were specific to data processing, data curation, metadata tools, or variant identifiers, they can be grouped into core recommendations addressing common challenges within the NHGRI resource community. These core recommendations highlight the key themes that emerged across sessions and are listed in the nine recommendations below. Increase transparency to enable effective sharing/reproducibility (documenting, benchmarking, publishing, mapping)Develop entity schema and ontology mapping tools (between models, identifiers, etc.)Annotate tools using resources to increase findability and reuse (Examples: EDAM Ontology of Bioscientific data analysis and data management)Use standard nomenclature and identifiersMake workflows usable by researchers with limited programming expertiseImplement APIs to improve data connectivityPresent data in an interpretable manner, along with machine readabilityDevelop artificial intelligence/machine learning (AI/ML) methods for scaling curation processesAssess the impact of resources using an independent group that can assess return on investment and impact to health and scientific advancement. An additional key collaborative outcome was the development of Appendix A, which outlines ongoing and future efforts, including additional workshops, webinars, and meetings through the listed events provided by the NHGRI resource community. We hope that these activities will enable further advances in the implementation of FAIR standards and continue to foster collaboration and exchange across NHGRI resources and the global community.

Spanning two decades, the Encyclopaedia of DNA Elements (ENCODE) is a collaborative research project that aims to identify all the functional elements in the human and mouse genomes. To best serve the scientific community, all data generated by the consortium is shared through a web-portal (https://www.encodeproject.org/) with no access restrictions. The fourth and final phase of the project added a diverse set of new samples (including those associated with human disease), and a wide range of new assays aimed at detection, characterization and validation of functional genomic elements. The ENCODE data portal hosts results from over 23,000 functional genomics experiments, over 800 functional elements characterization experiments (including in vivo transgenic enhancer assays, reporter assays and CRISPR screens) along with over 60,000 results of computational and integrative analyses (including imputations, predictions and genome annotations). The ENCODE Data Coordination Center (DCC) is responsible for development and maintenance of the data portal, along with the implementation and utilisation of the ENCODE uniform processing pipelines to generate uniformly processed data. Here we report recent updates to the data portal. Specifically, we have completely redesigned the home page, improved search interface, added several new pages to highlight collections of biologically related data (deeply profiled cell lines, immune cells, Alzheimer's Disease, RNA-Protein interactions, degron matrix and a matrix of experiments organised by human donors), added single-cell experiments, and enhanced the cart interface for visualisation and download of user-selected datasets.

Nearly 90% of the disease risk-associated variants identified from genome-wide association studies (GWAS) are in non-coding regions of the genome. The annotations obtained from analyzing functional genomics assays can provide additional information to pinpoint causal variants, which are often not the lead variants identified from association studies. However, the lack of available annotation tools limits the use of such data. To address the challenge, we have previously built the RegulomeDB database for prioritizing and annotating variants in non-coding regions[1][1], which has been a highly utilized resource for the research community (Supplementary Fig. 1). RegulomeDB annotates a variant by intersecting its position with genomic intervals identified from functional genomic assays and computational approaches. It also incorporates those hits of a variant into a heuristic ranking score, representing its potential to be functional in regulatory elements. Here we present a newer version of the RegulomeDB web server, RegulomeDB v2.1 (). We improve and boost annotation power by incorporating thousands of newly processed data from functional genomic assays in GRCh38 assembly, and now include probabilistic scores from the SURF algorithm that was the top performing non-coding variant predictor in CAGI 5[2][2]. We also provide interactive charts and genome browser views to allow users an easy way to perform exploratory analyses in different tissue contexts. ### Competing Interest Statement The authors have declared no competing interest. [1]: #ref-1 [2]: #ref-2

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

Towards the goal of identifying functional elements in the human genome, the fourth and final phase of the ENCODE consortium has newly profiled hundreds of human tissues using sequencing-based measurements of genomic activity such as ChIP-seq measures of transcription factor binding and histone modification. Chromatin state annotations created by segmentation and genome annotation (SAGA) methods such as Segway have emerged as the predominant integrative summary of such epigenomic data sets. Here, we present the ENCODE4 catalog of Segway annotations, a set of sample-specific genome-wide Segway chromatin state annotations for 234 ENCODE human biosamples inferred from 1,794 functional genomics experiments. We define an updated vocabulary of chromatin state terms that includes patterns of activity present only in a subset of samples or identified only with rarely-performed assays. We show that these ENCODE4 Segway annotations accurately capture both general and cell-type-specific regulatory patterns, and do so with substantially improved sensitivity relative to prior large-scale chromatin annotation sets. This catalog facilitates the downstream discovery of regulatory mechanisms which underlie diseases and traits identified by genome-wide association studies.

The Encyclopedia of DNA elements (ENCODE) project is a collaborative effort to create a comprehensive catalog of functional elements in the human genome. The current database comprises more than 19000 functional genomics experiments across more than 1000 cell lines and tissues using a wide array of experimental techniques to study the chromatin structure, regulatory and transcriptional landscape of the Homo sapiens and Mus musculus genomes. All experimental data, metadata, and associated computational analyses created by the ENCODE consortium are submitted to the Data Coordination Center (DCC) for validation, tracking, storage, and distribution to community resources and the scientific community. The ENCODE project has engineered and distributed uniform processing pipelines in order to promote data provenance and reproducibility as well as allow interoperability between genomic resources and other consortia. All data files, reference genome versions, software versions, and parameters used by the pipelines are captured and available via the ENCODE Portal. The pipeline code, developed using Docker and Workflow Description Language (WDL; https://openwdl.org/) is publicly available in GitHub, with images available on Dockerhub (https://hub.docker.com), enabling access to a diverse range of biomedical researchers. ENCODE pipelines maintained and used by the DCC can be installed to run on personal computers, local HPC clusters, or in cloud computing environments via Cromwell. Access to the pipelines and data via the cloud allows small labs the ability to use the data or software without access to institutional compute clusters. Standardization of the computational methodologies for analysis and quality control leads to comparable results from different ENCODE collections - a prerequisite for successful integrative analyses.

Abstract Summary Large-scale sharing of genomic quantification data requires standardized access interfaces. In this Global Alliance for Genomics and Health project, we developed RNAget, an API for secure access to genomic quantification data in matrix form. RNAget provides for slicing matrices to extract desired subsets of data and is applicable to all expression matrix-format data, including RNA sequencing and microarrays. Further, it generalizes to quantification matrices of other sequence-based genomics such as ATAC-seq and ChIP-seq. Availability and implementation https://ga4gh-rnaseq.github.io/schema/docs/index.html.

Following the 2019 workshop, From Genomes to Phenotype: Genomic Variation Identification, Association, and Function, hosted by the National Human Genome Research Institute, the Impact of Genomic Variation on Function (IGVF) Consortium was funded to understand how genomic variation influences phenotypes. This large-scale effort will enable the direction of future research on targets likely to yield gains in treatments and prevention of disease.

Free AccessLektürenCharles de Miramon, Benjamin Hitz, Nicolas Robin, Christine Hämmerling, Raúl AcostaCharles de MiramonSearch for more papers by this author, Benjamin HitzSearch for more papers by this author, Nicolas RobinSearch for more papers by this author, Christine HämmerlingSearch for more papers by this author, Raúl AcostaSearch for more papers by this authorPublished Online:Apr 2022https://doi.org/10.7788/hian.2022.30.1.116SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous article FiguresReferencesRelatedDetails Download book coverVolume 30Issue 1April 2022 ISSN: 0942-8704eISSN: 2194-4032HistoryPublished online:April 2022 PDF download

Evaluating the impact of genetic variants on transcriptional regulation is a central goal in biological science that has been constrained by reliance on a single reference genome. To address this, we constructed phased, diploid genomes for four cadaveric donors (using long-read sequencing) and systematically charted noncoding regulatory elements and transcriptional activity across more than 25 tissues from these donors. Integrative analysis revealed over a million variants with allele-specific activity, coordinated, locus-scale allelic imbalances, and structural variants impacting proximal chromatin structure. We relate the personal genome analysis to the ENCODE encyclopedia, annotating allele- and tissue-specific elements that are strongly enriched for variants impacting expression and disease phenotypes. These experimental and statistical approaches, and the corresponding EN-TEx resource, provide a framework for personalized functional genomics. decorations. The heatmap (center) shows the GWAS tag SNP enrichment of distal active CTCF+ cCREs. The tissues (colored according to the GTEx convention from Fig. 1) run along the bottom, and phenotypes (usually diseases, not explicitly labeled) run along the vertical axis. Around the heatmap, we show zoomed-in views highlighting various comparisons. At the bottom, we show that the tissue specificity of the enrichment for the trait Granulocyte % of Myeloid WBC is much stronger for AS versus non-AS cCREs. On the left, we show higher LDSC enrichment for AS elements compared to the corresponding non-AS ones for one tissue (coronary artery) across many associated traits. Left-bottom extends this comparison (for a single tissue-trait pair) to include roadmap annotations.

The Encyclopedia of DNA Elements (ENCODE) is an ongoing collaborative research project aimed at identifying all the functional elements in the human and mouse genomes. Data generated by the ENCODE consortium are freely accessible at the ENCODE portal (https://www.encodeproject. org/), which is developed and maintained by the ENCODE Data Coordinating Center (DCC). Since the initial portal release in 2013, the ENCODE DCC has updated the portal to make ENCODE data more findable, accessible, interoperable and reusable. Here, we report on recent updates, including new ENCODE data and assays, ENCODE uniform data processing pipelines, new visualization tools, a dataset cart feature, unrestricted public access to ENCODE data on the cloud (Amazon Web Services open data registry, https://registry.opendata.aws/encode-project!) and more comprehensive tutorials and documentation.

The Encylopedia of DNA Elements (ENCODE) Project launched in 2003 with the long-term goal of developing a comprehensive map of functional elements in the human genome. These included genes, biochemical regions associated with gene regulation (for example, transcription factor binding sites, open chromatin, and histone marks) and transcript isoforms. The marks serve as sites for candidatecis-regulatory elements (cCREs) that may serve functional roles in regulating gene expression(1). The project has been extended to model organisms, particularly the mouse. In the third phase of ENCODE, nearly a million and more than 300,000 cCRE annotations have been generated for human and mouse, respectively, and these have provided a valuable resource for the scientific community. The authors summarize the history of the ENCODE Project, the achievements of ENCODE 1 and ENCODE 2, and how the new data generated and analysed in ENCODE 3 complement the previous phases.

The Encyclopedia of DNA Elements (ENCODE) web portal hosts genomic data generated by the ENCODE Consortium, Genomics of Gene Regulation, The NIH Roadmap Epigenomics Consortium, and the modENCODE and modERN projects. The goal of the ENCODE project is to build a comprehensive map of the functional elements of the human and mouse genomes. Currently, the portal database stores over 500 TB of raw and processed data from over 15,000 experiments spanning assays that measure gene expression, DNA accessibility, DNA and RNA binding, DNA methylation, and 3D chromatin structure across numerous cell lines, tissue types, and differentiation states with selected genetic and molecular perturbations. The ENCODE portal provides unrestricted access to the aforementioned data and relevant metadata as a service to the scientific community. The metadata model captures the details of the experiments, raw and processed data files, and processing pipelines in human and machine-readable form and enables the user to search for specific data either using a web browser or programmatically via REST API. Furthermore, ENCODE data can be freely visualized or downloaded for additional analyses. © 2019 The Authors. Basic Protocol: Query the portal Support Protocol 1: Batch downloading Support Protocol 2: Using the cart to download files Support Protocol 3: Visualize data Alternate Protocol: Query building and programmatic access.

The Encyclopedia of DNA Elements (ENCODE) Consortium aims to discover and annotate functional elements in the human genome through functional genomic studies. ENCODE investigators have so far assayed over 75 different cell and tissue types with more than 40 high-throughput genomic technologies in over 15,000 experiments. ENCODE Data Coordinating Center (DCC) collects, organizes, curates and shares these experimental data along with results of uniform integrative analyses on the open access ENCODE portal (https://www.encodeproject.org). In order to maximize the use of data, ENCODE has established a set of data standards that make ENCODE results adherent to the FAIR (findable, accessible, interoperable and reusable) principles. These standards are coherent yet flexible enough to accommodate current and future technologies. Therefore, they have the potential to be applied to data from other consortium-based projects and ensure all projects are interoperable. Indeed, the ENCODE portal also hosts data from Roadmap, modENCODE, modERN and GGR using the same standards. Moreover, the ENCODE metadata data model is MINSEQE compliant and can be exported in ISA-TAB, GEO, SRA-XML or GA4GH-defined formats. ENCODE standards specify a set of metadata to be collected, which not only makes experimental materials reusable and uniquely identifiable but also encourages reproducibility and interpretability of experiments. ENCODE DCC modeled these metadata as a hybrid relational-object-based data store and implemented it in JSON-SCHEMA. Such an implementation allows curators to monitor the compliance with the ENCODE standards using JSON schema dependencies, audits, controlled vocabularies and ontologies. In addition to promoting data provenance, following data standards agreed upon by the community is a prerequisite for uniform data processing. Uniform data processing makes ENCODE experiments comparable within ENCODE as well as to experiments from other consortia. Overall, ENCODE data standards ensure the data quality and extend the use of high-throughput genomic data.

The Encyclopedia of DNA Elements (ENCODE) Data Coordinating Center has developed the ENCODE Portal database and website as the source for the data and metadata generated by the ENCODE Consortium. Two principles have motivated the design. First, experimental protocols, analytical procedures and the data themselves should be made publicly accessible through a coherent, web-based search and download interface. Second, the same interface should serve carefully curated metadata that record the provenance of the data and justify its interpretation in biological terms. Since its initial release in 2013 and in response to recommendations from consortium members and the wider community of scientists who use the Portal to access ENCODE data, the Portal has been regularly updated to better reflect these design principles. Here we report on these updates, including results from new experiments, uniformly-processed data from other projects, new visualization tools and more comprehensive metadata to describe experiments and analyses. Additionally, the Portal is now home to meta(data) from related projects including Genomics of Gene Regulation, Roadmap Epigenome Project, Model organism ENCODE (modENCODE) and modERN. The Portal now makes available over 13000 datasets and their accompanying metadata and can be accessed at: https://www.encodeproject.org/.

Prevention of unintended duplication is one of the ongoing challenges many databases have to address. Working with high-throughput sequencing data, the complexity of that challenge increases with the complexity of the definition of a duplicate. In a computational data model, a data object represents a real entity like a reagent or a biosample. This representation is similar to how a card represents a book in a paper library catalog. Duplicated data objects not only waste storage, they can mislead users into assuming the model represents more than the single entity. Even if it is clear that two objects represent a single entity, data duplication opens the door to potential inconsistencies between the objects since the content of the duplicated objects can be updated independently, allowing divergence of the metadata associated with the objects. Analogously to a situation in which a catalog in a paper library would contain by mistake two cards for a single copy of a book. If these cards are listing simultaneously two different individuals as current book borrowers, it would be difficult to determine which borrower (out of the two listed) actually has the book. Unfortunately, in a large database with multiple submitters, unintended duplication is to be expected. In this article, we present three principal guidelines the Encyclopedia of DNA Elements (ENCODE) Portal follows in order to prevent unintended duplication of both actual files and data objects: definition of identifiable data objects (I), object uniqueness validation (II) and de-duplication mechanism (III). In addition to explaining our modus operandi, we elaborate on the methods used for identification of sequencing data files. Comparison of the approach taken by the ENCODE Portal vs other widely used biological data repositories is provided.